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Pancreatic cancer is a drug resistant hypovascular tumor. Although there are many studies on the mechanism of chemoresistance in pancreatic cancers, studies on the relationship between ABCG2 and chemoresistance during hypoxia of pancreatic cancer are rare. Hypoxia-inducible factor-1 (HIF-1a) is a master regulator of the transcriptional response to oxygen deprivation in cancer cells. The aim of this study was to examine the role of ABCG 2 and HIF-1a in mediating chemoresistance during hypoxia in pancreatic cancer. In this study, we detected the expression levels of ABCG 2 , ERK/phosphorylated-ERK (p-ERK) and HIF-1a by immunohistochemistry in fresh pancreatic cancer and paracarcinoma tissues obtained from 25 patients. The mechanism by which p-ERK1/2 and HIF-1a affect ABCG 2 s expression was analyzed in the hypoxic cultured human pancreatic cancer cell line Capan-2. ABCG2-mediatedregulation of gemcitabine response under hypoxic conditions in pancreatic cancer cells was observed. It was found that ABCG 2 , ERK/p-ERK and HIF-1a were overexpressed in cancer tissues. ABCG2, HIF-1a and p-ERK levels were demonstrated to be high during hypoxic conditions in pancreatic cancer cells. Hypoxia induced phosphorylation of ERK1/2 to activate HIF-1a and contribute the ABCG 2 expression and mediated gemcitabine chemoresistance in pancreatic cancer cells. Hypoxic conditions induced HIF-1a binding to target gene sequences in the ABCG 2 promoter, resulting in increased transcription in pancreatic cancer cells. We demonstrated that hypoxia-induced chemoresistance is due to the regulation of ABCG 2 through the activation of ERK1/2/HIF-1a. ABCG 2 could serve as a predictor of gemcitabine response and, potentially, as a chemotherapeutic target in pancreatic cancer. Inhibition of ERK1/2 and HIF-1acould result in increased gemcitabine sensitization in pancreatic cancer with highly expressed ABCG 2 cell member protein.

Panel count data often occur in long-term studies that concern occurrence rate of a recurrent event. Methods have been proposed for regression analysis of panel count data, but most of the existing research focuses on situations where observation times are independent of longitudinal response variables and therefore rely on conditional inference procedures given the observation times. This article considers a different situation where the independence assumption may not hold. That is, the observation times and the response variable may be correlated. For inference, estimating equation approaches are proposed for estimation of regression parameters and both large and finite sample properties of the proposed estimates are established. An illustrative example from a cancer study is provided.

This paper discusses regression analysis of panel count data that often arise in longitudinal studies concerning occurrence rates of certain recurrent events. Panel count data mean that each study subject is observed only at discrete time points rather than under continuous observation. Furthermore, both observation and follow-up times can vary from subject to subject and may be correlated with the recurrent events. For inference, we propose some shared frailty models and estimating equations are developed for estimation of regression parameters. The proposed estimates are consistent and have asymptotically a normal distribution. The finite sample properties of the proposed estimates are investigated through simulation and an illustrative example from a cancer study is provided.

As a glycol-protein located in extracellular matrix (ECM), tenascin-C (TNC) is absent in most normal adult tissues but is highly expressed in the majority of malignant solid tumors. Pancreatic cancer is characterized by an abundant fibrous tissue rich in TNC. Although it was reported that TNC's expression increased in the progression from low-grade precursor lesions to invasive cancer and was associated with tumor differentiation in human pancreatic cancer, studies on the relations between TNC and tumor progression in pancreatic cancer were rare. In this study, we performed an analysis to determine the effects of TNC on modulating cell apoptosis and chemo-resistance and explored its mechanisms involving activation in pancreatic cancer cell. The expressions of TNC, ERK1/2/p-ERK1/2, Bcl-xL and Bcl-2 were detected by immunohistochemistry and western blotting. Then the effects of exogenous and endogenous TNC on the regulation of tumor proliferation, apoptosis and gemcitabine cytotoxicity were investigated. The associations among the TNC knockdown, TNC stimulation and expressions of ERK1/2/NF-κB/p65 and apoptotic regulatory proteins were also analyzed in cell lines. The mechanism of TNC on modulating cancer cell apoptosis and drug resistant through activation of ERK1/2/NF-κB/p65 signals was evaluated. The effect of TNC on regulating cell cycle distribution was also tested. TNC, ERK1/2/p-ERK1/2, and apoptotic regulatory proteins Bcl-xL and Bcl-2 were highly expressed in human pancreatic cancer tissues. In vitro, exogenous TNC promoted pancreatic cancer cell growth also mediates basal as well as starved and drug-induced apoptosis in pancreatic cancer cells. The effects of TNC on anti-apoptosis were induced by the activation state of ERK1/2/NF-κB/p65 signals in pancreatic cell. TNC phosphorylate ERK1/2 to induce NF-κB/p65 nucleus translocation. The latter contributes to promote Bcl-xL, Bcl-2 protein expressions and reduce caspase activity, which inhibit cell apoptotic processes. TNC mediated gemcitabine chemo-resistance via modulating cell apoptosis in pancreatic cancer. TNC resulted in the enrichment of pancreatic cancer cells in S-phase with a concomitant decrease in number of cells in G1 phase. The present study indicated TNC in cellular matrix induces an activation of ERK1/2/NF-κB/p65 signaling cascade and thereby mediates resistance to apoptosis in pancreatic cancer. TNC could serve as a diagnostic marker and predictor of gemcitabine response and potentially as a target for chemotherapy of pancreatic cancer.

Sparse learning is central to high-dimensional data analysis, and various methods have been developed. Ideally, a sparse learning method shall be methodologically flexible, computationally efficient, and with theoretical guarantee, yet most existing methods need to compromise some of these properties to attain the other ones. In this article, a three-step sparse learning method is developed, involving kernel-based estimation of the regression function and its gradient functions as well as a hard thresholding. Its key advantage is that it assumes no explicit model assumption, admits general predictor effects, allows for efficient computation, and attains desirable asymptotic sparsistency. The proposed method can be adapted to any reproducing kernel Hilbert space (RKHS) with different kernel functions, and its computational cost is only linear in the data dimension. The asymptotic sparsistency of the proposed method is established for general RKHS under mild conditions. Numerical experiments also support that the proposed method compares favorably against its competitors in both simulated and real examples.

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Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.